Global Illumination Compensation for Background Subtraction Using Gaussian-Based Background Difference Modeling

Abstract

This paper presents a background segmentation technique, which is able to process acceptable segmentation masks under fast global illumination changes. The histogram of the frame-based background difference is modeled with multiple kernels. The model that represents the histogram at best, is used to determine the shift in luminance due to global illumination or diaphragm changes, such that the background difference can be compensated. Experimental results have revealed that the number of incorrectly classified pixels using global illumination compensation instead of only the approximated median method reduces from 77% to 19% shortly after a fast change. The performance of the proposed technique is similar to state-of-the-art related work for global illumination changes, despite the fact that only luminance information is used. The algorithm is computationally simple and can operate at 30 frames-per-second for VGA resolution on a P-IV 3-GHz PC.